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| import gradio as gr | |
| from langchain_community.chat_models import ChatOpenAI | |
| from meta_prompt.sample_generator import TaskDescriptionGenerator | |
| def process_json(input_json, model_name, generating_batch_size, temperature): | |
| try: | |
| model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3) | |
| generator = TaskDescriptionGenerator(model) | |
| result = generator.process(input_json, generating_batch_size) | |
| description = result["description"] | |
| examples_directly = [[example["input"], example["output"]] for example in result["examples_directly"]["examples"]] | |
| input_analysis = result["examples_from_briefs"]["input_analysis"] | |
| new_example_briefs = result["examples_from_briefs"]["new_example_briefs"] | |
| examples_from_briefs = [[example["input"], example["output"]] for example in result["examples_from_briefs"]["examples"]] | |
| examples = [[example["input"], example["output"]] for example in result["additional_examples"]] | |
| return description, examples_directly, input_analysis, new_example_briefs, examples_from_briefs, examples | |
| except Exception as e: | |
| raise gr.Error(f"An error occurred: {str(e)}") | |
| def generate_description_only(input_json, model_name, temperature): | |
| try: | |
| model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3) | |
| generator = TaskDescriptionGenerator(model) | |
| description = generator.generate_description(input_json) | |
| return description | |
| except Exception as e: | |
| raise gr.Error(f"An error occurred: {str(e)}") | |
| def analyze_input(description, model_name, temperature): | |
| try: | |
| model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3) | |
| generator = TaskDescriptionGenerator(model) | |
| input_analysis = generator.analyze_input(description) | |
| return input_analysis | |
| except Exception as e: | |
| raise gr.Error(f"An error occurred: {str(e)}") | |
| def generate_briefs(description, input_analysis, generating_batch_size, model_name, temperature): | |
| try: | |
| model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3) | |
| generator = TaskDescriptionGenerator(model) | |
| briefs = generator.generate_briefs(description, input_analysis, generating_batch_size) | |
| return briefs | |
| except Exception as e: | |
| raise gr.Error(f"An error occurred: {str(e)}") | |
| def generate_examples_from_briefs(description, new_example_briefs, input_str, generating_batch_size, model_name, temperature): | |
| try: | |
| model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3) | |
| generator = TaskDescriptionGenerator(model) | |
| result = generator.generate_examples_from_briefs(description, new_example_briefs, input_str, generating_batch_size) | |
| examples = [[example["input"], example["output"]] for example in result["examples"]] | |
| return examples | |
| except Exception as e: | |
| raise gr.Error(f"An error occurred: {str(e)}") | |
| def generate_examples_directly(description, raw_example, generating_batch_size, model_name, temperature): | |
| try: | |
| model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3) | |
| generator = TaskDescriptionGenerator(model) | |
| result = generator.generate_examples_directly(description, raw_example, generating_batch_size) | |
| examples = [[example["input"], example["output"]] for example in result["examples"]] | |
| return examples | |
| except Exception as e: | |
| raise gr.Error(f"An error occurred: {str(e)}") | |
| def format_selected_example(evt: gr.SelectData, examples): | |
| if evt.index[0] < len(examples): | |
| selected_example = examples.iloc[evt.index[0]] # Use iloc to access by integer position | |
| json_example = json.dumps({"input": selected_example.iloc[0], "output": selected_example.iloc[1]}, indent=2, ensure_ascii=False) | |
| return json_example | |
| return "" | |
| with gr.Blocks(title="Task Description Generator") as demo: | |
| gr.Markdown("# Task Description Generator") | |
| gr.Markdown("Enter a JSON object with 'input' and 'output' fields to generate a task description and additional examples.") | |
| with gr.Row(): | |
| with gr.Column(scale=1): # Inputs column | |
| input_json = gr.Textbox(label="Input JSON", lines=10, show_copy_button=True) | |
| model_name = gr.Dropdown( | |
| label="Model Name", | |
| choices=["llama3-70b-8192", "llama3-8b-8192", "llama-3.1-70b-versatile", "llama-3.1-8b-instant", "gemma2-9b-it"], | |
| value="llama3-70b-8192" | |
| ) | |
| temperature = gr.Slider(label="Temperature", value=1.0, minimum=0.0, maximum=1.0, step=0.1) | |
| generating_batch_size = gr.Slider(label="Generating Batch Size", value=3, minimum=1, maximum=10, step=1) | |
| with gr.Row(): | |
| submit_button = gr.Button("Generate", variant="primary") | |
| generate_description_button = gr.Button("Generate Description", variant="secondary") | |
| with gr.Column(scale=1): # Outputs column | |
| description_output = gr.Textbox(label="Description", lines=5, show_copy_button=True) | |
| with gr.Row(): | |
| generate_examples_directly_button = gr.Button("Generate Examples Directly", variant="secondary") | |
| analyze_input_button = gr.Button("Analyze Input", variant="secondary") | |
| examples_directly_output = gr.DataFrame(label="Examples Directly", headers=["Input", "Output"], interactive=False) | |
| input_analysis_output = gr.Textbox(label="Input Analysis", lines=5, show_copy_button=True) | |
| generate_briefs_button = gr.Button("Generate Briefs", variant="secondary") | |
| example_briefs_output = gr.Textbox(label="Example Briefs", lines=5, show_copy_button=True) | |
| generate_examples_from_briefs_button = gr.Button("Generate Examples from Briefs", variant="secondary") | |
| examples_from_briefs_output = gr.DataFrame(label="Examples from Briefs", headers=["Input", "Output"], interactive=False) | |
| examples_output = gr.DataFrame(label="Examples", headers=["Input", "Output"], interactive=False) | |
| new_example_json = gr.Textbox(label="New Example JSON", lines=5, show_copy_button=True) | |
| clear_button = gr.ClearButton([input_json, description_output, input_analysis_output, | |
| example_briefs_output, examples_from_briefs_output, | |
| examples_output, new_example_json]) | |
| submit_button.click( | |
| fn=process_json, | |
| inputs=[input_json, model_name, generating_batch_size, temperature], | |
| outputs=[description_output, examples_directly_output, input_analysis_output, example_briefs_output, examples_from_briefs_output, examples_output] | |
| ) | |
| generate_description_button.click( | |
| fn=generate_description_only, | |
| inputs=[input_json, model_name, temperature], | |
| outputs=[description_output] | |
| ) | |
| generate_examples_directly_button.click( | |
| fn=generate_examples_directly, | |
| inputs=[description_output, input_json, generating_batch_size, model_name, temperature], | |
| outputs=[examples_directly_output] | |
| ) | |
| analyze_input_button.click( | |
| fn=analyze_input, | |
| inputs=[description_output, model_name, temperature], | |
| outputs=[input_analysis_output] | |
| ) | |
| generate_briefs_button.click( | |
| fn=generate_briefs, | |
| inputs=[description_output, input_analysis_output, generating_batch_size, model_name, temperature], | |
| outputs=[example_briefs_output] | |
| ) | |
| generate_examples_from_briefs_button.click( | |
| fn=generate_examples_from_briefs, | |
| inputs=[description_output, example_briefs_output, input_json, generating_batch_size, model_name, temperature], | |
| outputs=[examples_from_briefs_output] | |
| ) | |
| examples_directly_output.select( | |
| fn=format_selected_example, | |
| inputs=[examples_directly_output], | |
| outputs=[new_example_json] | |
| ) | |
| examples_from_briefs_output.select( | |
| fn=format_selected_example, | |
| inputs=[examples_from_briefs_output], | |
| outputs=[new_example_json] | |
| ) | |
| examples_output.select( | |
| fn=format_selected_example, | |
| inputs=[examples_output], | |
| outputs=[new_example_json] | |
| ) | |
| gr.Markdown("### Manual Flagging") | |
| with gr.Row(): | |
| flag_button = gr.Button("Flag") | |
| flag_reason = gr.Textbox(label="Reason for flagging") | |
| flagging_callback = gr.CSVLogger() | |
| flag_button.click( | |
| lambda *args: flagging_callback.flag(args), | |
| inputs=[input_json, model_name, generating_batch_size, description_output, examples_output, flag_reason], | |
| outputs=[] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |